scholarly journals IMU: A Content Replacement Policy for CCN, Based on Immature Content Selection

2021 ◽  
Vol 12 (1) ◽  
pp. 344
Author(s):  
Salman Rashid ◽  
Shukor Abd Razak ◽  
Fuad A. Ghaleb
Keyword(s):  
Network Performance ◽  
Rapid Change ◽  
Time Frame ◽  
Replacement Policy ◽  
Cache Replacement ◽  
Content Centric Networking ◽  
Content Selection ◽  
Data Freshness ◽  
Cache Hit Ratio ◽  
Cache Capacity

In-network caching is the essential part of Content-Centric Networking (CCN). The main aim of a CCN caching module is data distribution within the network. Each CCN node can cache content according to its placement policy. Therefore, it is fully equipped to meet the requirements of future networks demands. The placement strategy decides to cache the content at the optimized location and minimize content redundancy within the network. When cache capacity is full, the content eviction policy decides which content should stay in the cache and which content should be evicted. Hence, network performance and cache hit ratio almost equally depend on the content placement and replacement policies. Content eviction policies have diverse requirements due to limited cache capacity, higher request rates, and the rapid change of cache states. Many replacement policies follow the concept of low or high popularity and data freshness for content eviction. However, when content loses its popularity after becoming very popular in a certain period, it remains in the cache space. Moreover, content is evicted from the cache space before it becomes popular. To handle the above-mentioned issue, we introduced the concept of maturity/immaturity of the content. The proposed policy, named Immature Used (IMU), finds the content maturity index by using the content arrival time and its frequency within a specific time frame. Also, it determines the maturity level through a maturity classifier. In the case of a full cache, the least immature content is evicted from the cache space. We performed extensive simulations in the simulator (Icarus) to evaluate the performance (cache hit ratio, path stretch, latency, and link load) of the proposed policy with different well-known cache replacement policies in CCN. The obtained results, with varying popularity and cache sizes, indicate that our proposed policy can achieve up to 14.31% more cache hits, 5.91% reduced latency, 3.82% improved path stretch, and 9.53% decreased link load, compared to the recently proposed technique. Moreover, the proposed policy performed significantly better compared to other baseline approaches.

A Cache Replacement Policy for Location Dependent Data in Mobile Environments

2012 ◽  
pp. 286-295
Author(s):  
Mary Magdalene Jane. F ◽  
R Nadarajan ◽  
Maytham Safar
Keyword(s):  
Experimental Results ◽  
Data Availability ◽  
Replacement Policy ◽  
User Preference ◽  
Cache Replacement ◽  
Local Data ◽  
Performance Metric ◽  
Cache Replacement Policy ◽  
Replacement Scheme ◽  
Cache Hit Ratio

Cache hit ratio is employed as the primary performance metric where the higher the cache hit ratio, the higher the local data availability, lower uplink and downlink costs and less battery consumption. The experimental results show that the proposed replacement scheme is effective when the user preference is considered and the policy significantly outperforms the conventional replacement policies.

scholarly journals T-Move: A Light-Weight Protocol for Improved QoS in Content-Centric Networks with Producer Mobility

2019 ◽  
Vol 11 (2) ◽  
pp. 28 ◽  
Author(s):  
Swaroopa Korla ◽  
Shanti Chilukuri
Keyword(s):  
Network Performance ◽  
Cache Replacement ◽  
Light Weight ◽  
Content Centric Networking ◽  
Recent Interest ◽  
Control Message ◽  
Novel Concept ◽  
Application Specific ◽  
Message Overhead

Recent interest in applications where content is of primary interest has triggered the exploration of a variety of protocols and algorithms. For such networks that are information-centric, architectures such as the Content-Centric Networking have been proven to result in good network performance. However, such architectures are still evolving to cater for application-specific requirements. This paper proposes T-Move, a light-weight solution for producer mobility and caching at the edge that is especially suitable for content-centric networks with mobile content producers. T-Move introduces a novel concept called trendiness of data for Content-Centric Networking (CCN)/Named Data Networking (NDN)-based networks. It enhances network performance and quality of service (QoS) using two strategies—cache replacement and proactive content-pushing for handling producer mobility—both based on trendiness. It uses simple operations and smaller control message overhead and is suitable for networks where the response needs to be quick. Simulation results using ndnSIM show reduced traffic, content retrieval time, and increased cache hit ratio with T-Move, when compared to MAP-Me and plain NDN for networks of different sizes and mobility rates.

scholarly journals An Optimal Routing Algorithm in Service Customized 5G Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Haipeng Yao ◽  
Chao Fang ◽  
Yiru Guo ◽  
Chenglin Zhao
Keyword(s):  
Big Data ◽  
Network Architecture ◽  
Network Performance ◽  
Routing Algorithm ◽  
Optimal Routing ◽  
Cache Replacement ◽  
Data Traffic ◽  
Average Response ◽  
5G Networks ◽  
Content Centric Networking

With the widespread use of Internet, the scale of mobile data traffic grows explosively, which makes 5G networks in cellular networks become a growing concern. Recently, the ideas related to future network, for example, Software Defined Networking (SDN), Content-Centric Networking (CCN), and Big Data, have drawn more and more attention. In this paper, we propose a service-customized 5G network architecture by introducing the ideas of separation between control plane and data plane, in-network caching, and Big Data processing and analysis to resolve the problems traditional cellular radio networks face. Moreover, we design an optimal routing algorithm for this architecture, which can minimize average response hops in the network. Simulation results reveal that, by introducing the cache, the network performance can be obviously improved in different network conditions compared to the scenario without a cache. In addition, we explore the change of cache hit rate and average response hops under different cache replacement policies, cache sizes, content popularity, and network topologies, respectively.

Enabling Service Cache in Edge Clouds

2021 ◽  
Vol 2 (3) ◽  
pp. 1-24
Author(s):  
Chih-Kai Huang ◽  
Shan-Hsiang Shen
Keyword(s):  
Virtual Machines ◽  
Replacement Policy ◽  
Cache Replacement ◽  
Cache Replacement Policy ◽  
Average Latency ◽  
Hit Rates ◽  
Iot Devices ◽  
Multi Access ◽  
Computational Resources ◽  
The Cost

The next-generation 5G cellular networks are designed to support the internet of things (IoT) networks; network components and services are virtualized and run either in virtual machines (VMs) or containers. Moreover, edge clouds (which are closer to end users) are leveraged to reduce end-to-end latency especially for some IoT applications, which require short response time. However, the computational resources are limited in edge clouds. To minimize overall service latency, it is crucial to determine carefully which services should be provided in edge clouds and serve more mobile or IoT devices locally. In this article, we propose a novel service cache framework called S-Cache , which automatically caches popular services in edge clouds. In addition, we design a new cache replacement policy to maximize the cache hit rates. Our evaluations use real log files from Google to form two datasets to evaluate the performance. The proposed cache replacement policy is compared with other policies such as greedy-dual-size-frequency (GDSF) and least-frequently-used (LFU). The experimental results show that the cache hit rates are improved by 39% on average, and the average latency of our cache replacement policy decreases 41% and 38% on average in these two datasets. This indicates that our approach is superior to other existing cache policies and is more suitable in multi-access edge computing environments. In the implementation, S-Cache relies on OpenStack to clone services to edge clouds and direct the network traffic. We also evaluate the cost of cloning the service to an edge cloud. The cloning cost of various real applications is studied by experiments under the presented framework and different environments.

scholarly journals Analysis of rail yard and terminal performances

2014 ◽  
Vol 8 (2) ◽  
pp. 178-200 ◽  
Author(s):  
Marin Marinov ◽  
Leonardo Di Giovanni ◽  
Giulia Bellisai ◽  
Julian Clevermann ◽  
Anastasia Mastellou ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Critical Points ◽  
Network Performance ◽  
Logical Consequence ◽  
Time Frame ◽  
Key Factors ◽  
Railway Network ◽  
Rail Infrastructure ◽  
Different Types ◽  
Junction Points

One of the latest trends in the transport field is the increasing interest for the rejuvenation of the railway. It is considered to be a logical consequence of the gradual switch towards a more sustainable future in transports. Terminals and stations are considered to be the junction points between the various lines that constitute the railway network and can simply be described as points of arrival, departure and interchange of passengers or commodities. The most commonly used indicators that measure the level of their performance are time and cost. This study aims at exploring possible improvements that could be implemented to the infrastructure and the operation of terminals and stations in order to increase the efficiency level. Firstly, drawing upon grounded theory about rail infrastructure and terminals, a description is conducted, followed by a comparative analysis of the different types of existing terminals and stations. Secondly, the suggested improvements are presented in accordance with their time frame completion. The main contribution of this study is to illustrate the high significance of terminals, stations and yards, acknowledging them as crucial parts of the railway network, because as characterized and demonstrated in this study, their performance are key factors to the whole network performance, making the identification of their critical points and respective possible solutions, the final objective of this paper. In addition to this, emphasis is given to the need of improving and developing the existing terminal infrastructure and operations.

scholarly journals An adaptive cache replacement policy based on fine-grain reusability monitor

2018 ◽  
Vol 15 (2) ◽  
pp. 20171099-20171099 ◽  
Author(s):  
Duk-Jun Bang ◽  
Min-Kwan Kee ◽  
Hong-Yeol Lim ◽  
Gi-Ho Park
Keyword(s):  
Replacement Policy ◽  
Cache Replacement ◽  
Fine Grain ◽  
Cache Replacement Policy ◽  
Adaptive Cache

scholarly journals A comprehensive evaluation of cache utilization characteristics in large scale WSN considering network driven cache replacement techniques

2018 ◽  
Vol 188 ◽  
pp. 05004
Author(s):  
Christos Panagiotou ◽  
Christos Antonopoulos ◽  
Stavros Koubias
Keyword(s):  
Smart City ◽  
Large Scale ◽  
Network Performance ◽  
Comprehensive Evaluation ◽  
Resource Conservation ◽  
Data Sources ◽  
Data Cache ◽  
Cache Replacement ◽  
Large Scale Networks ◽  
Result Analysis

WSNs as adopted in current smart city deployments, must address demanding traffic factors and resilience in failures. Furthermore, caching of data in WSN can significantly benefit resource conservation and network performance. However, data sources generate data volumes that could not fit in the restricted data cache resources of the caching nodes. This unavoidably leads to data items been evicted and replaced. This paper aims to experimentally evaluate the prominent caching techniques in large scale networks that resemble the Smart city paradigm regarding network performance with respect to critical application and network parameters. Through respective result analysis valuable insights are provided concerning the behaviour of caching in typical large scale WSN scenarios.

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